http://www.cnr.it/ontology/cnr/individuo/prodotto/ID8475
Combining network modeling and gene expression microarray analysis to explore the dynamics of Th1 and Th2 cell regulation (Articolo in rivista)
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- Combining network modeling and gene expression microarray analysis to explore the dynamics of Th1 and Th2 cell regulation (Articolo in rivista) (literal)
- Anno
- 2010-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1371/journal.pcbi.1001032 (literal)
- Alternative label
PEDICINI M, BARRENÄS F, CLANCY T, CASTIGLIONE F, HOVIG E, KANDURI K, SANTONI D, BENSON M (2010)
Combining network modeling and gene expression microarray analysis to explore the dynamics of Th1 and Th2 cell regulation
in PLoS computational biology
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- PEDICINI M, BARRENÄS F, CLANCY T, CASTIGLIONE F, HOVIG E, KANDURI K, SANTONI D, BENSON M (literal)
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- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- 1 Istituto per le Applicazioni del Calcolo \"Mauro Picone\", Consiglio Nazionale delle Ricerche (CNR), Rome, Italy, 2 The Unit for Clinical Systems Biology, University of Gothenburg, Gothenburg, Sweden, 3 Department of Tumor Biology, Institute of Cancer Research, the Norwegian Radium Hospital, Oslo, Norway, 4 The Institute for Medical Informatics, Rikshospitalet, Oslo University Hospital, Oslo, Norway, 5 Department of Informatics, University of Oslo, Oslo, Norway, 6 Barcelona Institute for Research in Biomedicine (IRB), Barcelona Science Park, Barcelona, Spain, 7 Unit for Pediatric Allergology, Queen Silvia Children's Hospital, Gothenburg, Sweden (literal)
- Titolo
- Combining network modeling and gene expression microarray analysis to explore the dynamics of Th1 and Th2 cell regulation (literal)
- Abstract
- Two T helper (Th) cell subsets, namely Th1 and Th2 cells, play an important role in inflammatory diseases. The two subsets
are thought to counter-regulate each other, and alterations in their balance result in different diseases. This paradigm has
been challenged by recent clinical and experimental data. Because of the large number of genes involved in regulating Th1
and Th2 cells, assessment of this paradigm by modeling or experiments is difficult. Novel algorithms based on formal
methods now permit the analysis of large gene regulatory networks. By combining these algorithms with in silico knockouts
and gene expression microarray data from human T cells, we examined if the results were compatible with a counterregulatory
role of Th1 and Th2 cells. We constructed a directed network model of genes regulating Th1 and Th2 cells
through text mining and manual curation. We identified four attractors in the network, three of which included genes that
corresponded to Th0, Th1 and Th2 cells. The fourth attractor contained a mixture of Th1 and Th2 genes. We found that
neither in silico knockouts of the Th1 and Th2 attractor genes nor gene expression microarray data from patients with
immunological disorders and healthy subjects supported a counter-regulatory role of Th1 and Th2 cells. By combining
network modeling with transcriptomic data analysis and in silico knockouts, we have devised a practical way to help unravel
complex regulatory network topology and to increase our understanding of how network actions may differ in health and
disease. (literal)
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